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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import shutil\n",
    "\n",
    "def is_blurry(image, threshold=20):\n",
    "    # 使用拉普拉斯算子检测模糊\n",
    "    laplacian = cv2.Laplacian(image, cv2.CV_64F).var()\n",
    "    return laplacian < threshold\n",
    "\n",
    "def is_dark(image, brightness_threshold=100):\n",
    "    # 转换到 HSV 色彩空间\n",
    "    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "    # 计算亮度通道的平均值\n",
    "    _, _, v = cv2.split(hsv)\n",
    "    mean_brightness = np.mean(v)\n",
    "    return mean_brightness < brightness_threshold\n",
    "\n",
    "def augment_images(images_dir, output_dir):\n",
    "    # 如果输出目录存在，则清空，否则创建新目录\n",
    "    if os.path.exists(output_dir):\n",
    "        shutil.rmtree(output_dir)\n",
    "    os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "    # 获取目录中的所有图像文件名\n",
    "    image_files = [f for f in os.listdir(images_dir) if f.endswith('.jpg') or f.endswith('.jpeg')]\n",
    "    # 遍历所有图像文件\n",
    "    for image_file in image_files:\n",
    "        # 打开图像文件\n",
    "        img_path = os.path.join(images_dir, image_file)\n",
    "        img = cv2.imread(img_path)\n",
    "\n",
    "        # 检查图像是否模糊\n",
    "        if is_blurry(img) or img.shape[0] < 150 or img.shape[1] < 150 or is_dark(img):\n",
    "            # print(img_path)\n",
    "            # cv2.imwrite(os.path.join(\"../Dataset/discard_photos\", image_file), img)\n",
    "            continue\n",
    "\n",
    "        # 随机添加噪声、模糊、颜色变换等\n",
    "        if np.random.randint(0, 3) == 0:\n",
    "            img = cv2.GaussianBlur(img, (3, 3), 0)\n",
    "        if np.random.randint(0, 3) == 0:\n",
    "            hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "            hsv[..., 0] = np.clip(hsv[..., 0].astype(int) + np.random.randint(-5, 5), 0, 179)\n",
    "            hsv[..., 1] = np.clip(hsv[..., 1].astype(int) + np.random.randint(-15, 15), 0, 255)\n",
    "            hsv[..., 2] = np.clip(hsv[..., 2].astype(int) + np.random.randint(-25, 25), 0, 255)\n",
    "            img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
    "\n",
    "        # 保存增强后的图像\n",
    "        cv2.imwrite(os.path.join(output_dir, f\"aug_{image_file}\"), img)\n",
    "\n",
    "images_dir = '../Dataset/photos/3001/'\n",
    "output_dir = '../Dataset/augmented_photos/3001'\n",
    "augment_images(images_dir, output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'17485': 0, '2456': 1, '2730': 2, '3001': 3, '3002': 4, '3003': 5, '3004': 6, '3006': 7, '3007': 8, '3009': 9, '3010': 10, '30414': 11, '3622': 12, '4600': 13, '6111': 14, '87079': 15}\n",
      "Epoch 1/20, Loss: 1.8688180017535838\n",
      "Epoch 2/20, Loss: 1.3501635281739603\n",
      "Epoch 3/20, Loss: 1.1931923853689665\n",
      "Epoch 4/20, Loss: 1.1039456704150032\n",
      "Epoch 5/20, Loss: 1.046197960760661\n",
      "Epoch 6/20, Loss: 1.0042561634628957\n",
      "Epoch 7/20, Loss: 0.9695747367261388\n",
      "Epoch 8/20, Loss: 0.9403432870588703\n",
      "Epoch 9/20, Loss: 0.9191266004706913\n",
      "Epoch 10/20, Loss: 0.8974268259795657\n",
      "Epoch 11/20, Loss: 0.8827400753559376\n",
      "Epoch 12/20, Loss: 0.866985842000809\n",
      "Epoch 13/20, Loss: 0.8501224895291464\n",
      "Epoch 14/20, Loss: 0.8434784568049749\n",
      "Epoch 15/20, Loss: 0.8285353603963116\n",
      "Epoch 16/20, Loss: 0.8223756243637353\n",
      "Epoch 17/20, Loss: 0.8068088214839424\n",
      "Epoch 18/20, Loss: 0.7992068046807275\n",
      "Epoch 19/20, Loss: 0.7931198143829996\n",
      "Epoch 20/20, Loss: 0.7844932316039993\n",
      "Training complete\n",
      "Accuracy on the test set: 74.72%\n",
      "Model saved to ../model/resnet50_with_renders.pth\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision import datasets, models\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "# 设置全局参数\n",
    "modellr = 1e-4\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 20\n",
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# 数据预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "\n",
    "])\n",
    "\n",
    "# 读取样本和标签\n",
    "data_set = datasets.ImageFolder('../Dataset/photos', transform)\n",
    "print(data_set.class_to_idx)\n",
    "\n",
    "# 随机划分数据集\n",
    "train_ratio = 0.8\n",
    "train_size = int(train_ratio * len(data_set))\n",
    "test_size = len(data_set) - train_size\n",
    "train_set, test_set = random_split(data_set, [train_size, test_size])\n",
    "\n",
    "# 导入数据\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# 实例化\n",
    "model = models.resnet50(pretrained=True)\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "num_ftrs = model.fc.in_features\n",
    "num_classes = len(data_set.classes)\n",
    "model.fc = nn.Linear(num_ftrs, num_classes)\n",
    "model = model.to(DEVICE)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=modellr)  # 选择简单暴力的Adam优化器，学习率调低\n",
    "\n",
    "def adjust_learning_rate(optimizer, epoch, initial_lr):\n",
    "    \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n",
    "    lr = initial_lr * (0.1 ** (epoch // 30))\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "\n",
    "\n",
    "# 训练模型\n",
    "def train_model(model, criterion, optimizer, train_loader, epochs, device, initial_lr):\n",
    "    model.train()\n",
    "    for epoch in range(epochs):\n",
    "        adjust_learning_rate(optimizer, epoch, initial_lr)\n",
    "        running_loss = 0.0\n",
    "        for inputs, labels in train_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            running_loss += loss.item()\n",
    "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {running_loss/len(train_loader)}\")\n",
    "    print(\"Training complete\")\n",
    "\n",
    "\n",
    "# 测试模型\n",
    "def test_model(model, test_loader, device):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in test_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            outputs = model(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    accuracy = 100 * correct / total\n",
    "    print(f\"Accuracy on the test set: {accuracy:.2f}%\")\n",
    "\n",
    "# 训练和测试模型\n",
    "train_model(model, criterion, optimizer, train_loader, EPOCHS, DEVICE, modellr)\n",
    "test_model(model, test_loader, DEVICE)\n",
    "\n",
    "# 保存模型\n",
    "model_save_path = \"../model/resnet50_with_renders.pth\"\n",
    "torch.save(model.state_dict(), model_save_path)\n",
    "print(f\"Model saved to {model_save_path}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All images processed and saved in the output directory.\n"
     ]
    }
   ],
   "source": [
    "# 模型预测效果\n",
    "from PIL import Image, ImageDraw\n",
    "import os\n",
    "import torch\n",
    "from torchvision import transforms, models\n",
    "\n",
    "# 模型路径\n",
    "model_path = '../model/resnet50_with_renders.pth' \n",
    "image_dir = '../Dataset/test/4'\n",
    "output_dir = '../Dataset/test_result' \n",
    "num_classes = 16\n",
    "\n",
    "\n",
    "# 创建模型实例\n",
    "model = models.resnet50(pretrained=True)\n",
    "num_ftrs = model.fc.in_features\n",
    "model.fc = torch.nn.Linear(num_ftrs, num_classes)  # <num_classes> 需要替换为类别数量\n",
    "\n",
    "# 加载模型的 state_dict\n",
    "model.load_state_dict(torch.load(model_path))\n",
    "model.eval()\n",
    "\n",
    "# 预处理函数\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "])\n",
    "\n",
    "# 加载并预处理图片\n",
    "def load_and_preprocess_image(image_path):\n",
    "    image = Image.open(image_path)\n",
    "    image = transform(image).unsqueeze(0)\n",
    "    return image\n",
    "\n",
    "# 预测函数\n",
    "def predict(model, image_tensor):\n",
    "    outputs = model(image_tensor)\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    return predicted.item()\n",
    "\n",
    "# 对文件夹中的每张图像进行预测，并在图像上写入预测结果\n",
    "for filename in os.listdir(image_dir):\n",
    "    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
    "        image_path = os.path.join(image_dir, filename)\n",
    "        image_tensor = load_and_preprocess_image(image_path)\n",
    "        predicted_class_idx = predict(model, image_tensor)\n",
    "\n",
    "        # 在图像上写入预测结果\n",
    "        output_image = Image.open(image_path)\n",
    "        draw = ImageDraw.Draw(output_image)\n",
    "        draw.text((10, 30), f'Predicted: {predicted_class_idx}', fill=(255, 0, 0))\n",
    "\n",
    "        # 保存修改后的图像\n",
    "        output_path = os.path.join(output_dir, filename)\n",
    "        output_image.save(output_path)\n",
    "\n",
    "print(\"All images processed and saved in the output directory.\")"
   ]
  }
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